Randomised controlled trial example
INTERACT: INvestigation of TExt message Reminders on
Adherence to Cardiac Treatment -
1.4.2012 to 1.9.2014
Lowering blood pressure and cholesterol can reduce the risk of heart
attacks and strokes, but about half of all patients prescribed medication
discontinue it after about two years, leading to many thousands of
avoidable deaths. Could some of these be avoided if people were sent text
messages to remind them to take their medicine? To test this, anyone with a
mobile phone prescribed cholesterol-lowering or blood pressure lowering
treatment for the prevention of cardiovascular disease was invited to join
a randomised controlled trial starting in April 2012. The aim was to
recruit 300 participants who would be divided into a text-message group of
150 and a control group of 150.
Participants were randomly allocated to receiving a programme of text
messages or to be part of a
control group that did not. Text reminders were
sent, first daily and then weekly, for a year to the first group. People
who received texts were to text back to say if they had already taken the
medication that day, had been reminded to take it, or had not yet taken it.
People who had not yet taken it were telephoned later to see how they were
getting on. "The intervention aims to remind, train and identify
individuals who are not-adhering, so action can be taken to correct
problems as they arise".
Both groups were assessed after six months ad after eighteen months. A
questionnaire assessed adherence to the medication and measurements of
blood pressure and blood cholesterol were taken.
The trial is expected to be completed by 1.9.2014, but results of a six-
month pilot study with 64 participants have been published. The results
were that
One patient out of 32 in the text message group discontinued their
medication
Five out of 29 in the control group discontinued their medication and
3 were "lost to follow-up".
Among those who continued their medication, the reported number of days
medications were missed in the four weeks prior to follow-up were 0.7 in
the text message group and 4.1 in the control arm; a difference of 3.4 days
(1.7 to 5.1)
Cholesterol and blood pressure were lower in the text message group.
The report concluded that the pilot study suggested that text massaging
"would be expected to reduce the risk of ischaemic heart disease by 48% and
stroke by 47%" and that "These results now need to be confirmed in a larger
trial."
Experimental Hypothesis
A hypothesis is an idea or theory that predicts what might happen. An
experimental hypothesis is a prediction, made to be tested, that one thing
(
variable) will affect
another.
The hypothesis will suggest that when one of two variables alters, the
other will as well.
Independent and Dependent Variables
The first variable (the one we alter to
affect the other) is called the independent variable. The variable we
predict will be altered is called the dependent variable.
Example "Drug x is good for making people with colds better"
predicts that the independent variable "Drug x" will tend to make the
dependent variable, "people with colds", better.
Non-directional hypotheses A non-directional hypothesis does not
predict which way the independent
variable will affect the dependent variable.
Example "Drug x will have an affect on people's colds"
This does not predict if it will make the colds better or worse.
Directional hypotheses A directional hypothesis predicts the way
the independent variable will affect the dependent variable.
Examples:
"Drug x will make people's colds worse"
OR
"Drug x will make people's colds better"
The slang term for a non-directional hypothesis is a two-tailed
hypothesis. A directional hypothesis is called a
one-tailed hypothesis. As with tossing a coin that has one head and
one tail, only one out of two predictions can be correct. But the coin
enalogy is misleading, because in the experiment
there is the third possibility that the independent variable may have no
effect. The drug may not
influence the colds in any way. This possibility is called the
null hypothesis
Statistical Tests
Tests of Significance
Statistical tests are used to see if
samples
of numbers appear to have come from
one or from two
populations. The
statistical test will also say what the likely margin of error is.
Another way of saying this, is that the statistical test tests whether
a difference observed between two samples is likely to reflect a real
difference between two populations.
For example:
One sample of twenty people with colds was given drug x. A control
sample of twenty people with colds was not given any treatment. 10 of the
first sample had stopped sneezing two hours after taking drug x. At the
same time, 8 of the control sample were found to have stopped sneezing.
It would appear that drug x works. But is the difference between
samples likely to reflect a real difference between the (hypothetical)
population
of all people with colds who might take drug x, and the
(hypothetical) population of the (same) people with colds, but without
taking drug x?
Null Hypothesis
The
Null Hypothesis is a hypothesis that, (despite the difference
between the
samples), there is no difference between the populations.
This
would mean that any difference we have observed between the samples is due
to sampling error.
The Null Hypothesis is contrasted with the
Alternative Hypothesis
The Alternative Hypothesis is the hypothesis we will accept if the Null
Hypothesis is rejected.
Tests of significance set up a model that is based on the assumption
that there is no difference between the populations.
This "null hypothesis"
is only rejected if it is very difficult to fit the data to such a model.
If this proves to be the case, the Alternative Hypothesis is accepted.
Conclusions about the Experimental Hypothesis
If it is found, by a statistical test of significance, that a
difference between two samples reflects a real difference between two
population, this is said in a way that shows how
reliable the conclusion
is.
For Example:
"The results of the statistical analysis were significant at the
p is less than or equal to 0.05
level".
This means that there is a 5% chance (p = probability) that, despite
the difference observed between the two samples, there is no difference
between the populations, and a 95% chance that the difference
observed
between the two samples reflects a real difference.
Study
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